Novel lung imaging biomarkers and skin gene expression subsetting in dasatinib treatment of systemic sclerosis-associated interstitial lung disease

Viktor Martyanov, Grace-Hyun J Kim, Wendy Hayes, Shuyan Du, Bishu J Ganguly, Oumar Sy, Sun Ku Lee, Galina S Bogatkevich, Gary L Schieven, Elena Schiopu, Roberta Gonçalves Marangoni, Jonathan Goldin, Michael L Whitfield, John Varga, Viktor Martyanov, Grace-Hyun J Kim, Wendy Hayes, Shuyan Du, Bishu J Ganguly, Oumar Sy, Sun Ku Lee, Galina S Bogatkevich, Gary L Schieven, Elena Schiopu, Roberta Gonçalves Marangoni, Jonathan Goldin, Michael L Whitfield, John Varga

Abstract

Background: There are no effective treatments or validated clinical response markers in systemic sclerosis (SSc). We assessed imaging biomarkers and performed gene expression profiling in a single-arm open-label clinical trial of tyrosine kinase inhibitor dasatinib in patients with SSc-associated interstitial lung disease (SSc-ILD).

Methods: Primary objectives were safety and pharmacokinetics. Secondary outcomes included clinical assessments, quantitative high-resolution computed tomography (HRCT) of the chest, serum biomarker assays and skin biopsy-based gene expression subset assignments. Clinical response was defined as decrease of >5 or >20% from baseline in the modified Rodnan Skin Score (MRSS). Pulmonary function was assessed at baseline and day 169.

Results: Dasatinib was well-tolerated in 31 patients receiving drug for a median of nine months. No significant changes in clinical assessments or serum biomarkers were seen at six months. By quantitative HRCT, 65% of patients showed no progression of lung fibrosis, and 39% showed no progression of total ILD. Among 12 subjects with available baseline and post-treatment skin biopsies, three were improvers and nine were non-improvers. Improvers mapped to the fibroproliferative or normal-like subsets, while seven out of nine non-improvers were in the inflammatory subset (p = 0.0455). Improvers showed stability in forced vital capacity (FVC) and diffusing capacity for carbon monoxide (DLCO), while both measures showed a decline in non-improvers (p = 0.1289 and p = 0.0195, respectively). Inflammatory gene expression subset was associated with higher baseline HRCT score (p = 0.0556). Non-improvers showed significant increase in lung fibrosis (p = 0.0313).

Conclusions: In patients with SSc-ILD dasatinib treatment was associated with acceptable safety profile but no significant clinical efficacy. Patients in the inflammatory gene expression subset showed increase in skin fibrosis, decreasing pulmonary function and worsening lung fibrosis during the study. These findings suggest that target tissue-specific gene expression analyses can help match patients and therapeutic interventions in heterogeneous diseases such as SSc, and quantitative HRCT is useful for assessing clinical outcomes.

Trial registration: Clinicaltrials.gov NCT00764309.

Conflict of interest statement

Competing Interests: VM, GSB, ES and RGM have no competing interests. GJK has received consulting fees from MedQIA LLC and has filed patents for quantitative imaging biomarkers in ILD. WH, SD, OS, SKL and GLS are employees of Bristol-Myers Squibb and own its stock. BJG is a former employee of Bristol-Myers Squibb and a current employee of Rinat/Pfizer. JG is a Founder of MedQIA LLC. MLW has filed patents for gene expression biomarkers in SSc, is a Scientific Founder of Celdara Medical LLC and has received consulting fees from Bristol-Myers Squibb. JV has received consulting fees from Bristol-Myers Squibb, Boehringer Ingelheim, Astellas, and research support from Bristol-Myers Squibb, Biogen, Novartis, Takeda and Cureveda. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Figures

Fig 1. CONSORT flowchart for the study.
Fig 1. CONSORT flowchart for the study.
Fig 2. Drop plots of changes in…
Fig 2. Drop plots of changes in texture-based quantitative scores from baseline using HRCT.
(A) QLF and (B) QILD for the most severe lobe. (C) QLF and (D) QILD for the whole lung. Dashed lines of ±3% and ±2% are indicators of thresholds for changes in regional analysis in lobe and whole lung, respectively.
Fig 3. Representative pairs of baseline and…
Fig 3. Representative pairs of baseline and follow-up HRCT images.
(A) Better case: pulmonary fibrosis (PF) improved and ground glass opacity (GGO) worsened by the visual assessment in whole lung; (B) annotated HRCT of (A) with a CAD system: QLF and QILD in whole lung decreased, by 2.9% (3.9% to 1.0%) and 0.2% (19.9% to 19.7%), respectively. (C) Stable case; (D) annotated CAD of (C): QLF in the worst lobe (lower left) increased by 2% (64% to 66%). In whole lung, QLF and QILD increased, by 2.3% (39.5% to 41.8%) and 1.8% (72.4% to 74.2%), respectively. (E) Worse case: PF worsened and GGO improved; (F) annotated CAD of (E): QLF increased by 6.0% (28% to 34%) in the most severe lobe (lower right) and 1.8% (9.8% to 11.6%) in whole lung. QILD in whole lung was stable (41.3% to 40.5%).
Fig 4. Intrinsic gene expression subset assignment…
Fig 4. Intrinsic gene expression subset assignment and response status.
(A) Intrinsic gene analysis. 3,207 probes (2,532 unique genes) at FDR

Fig 5. Baseline and improver gene expression…

Fig 5. Baseline and improver gene expression and pathway enrichment analysis.

(A) 1,062 genes were…

Fig 5. Baseline and improver gene expression and pathway enrichment analysis.
(A) 1,062 genes were differentially expressed (p

Fig 6. HRCT trends across improvers and…

Fig 6. HRCT trends across improvers and non-improvers.

(A) QGG trends. (B) QLF trends. (C)…

Fig 6. HRCT trends across improvers and non-improvers.
(A) QGG trends. (B) QLF trends. (C) Baseline [QGG-QLF] marker vs. intrinsic subset. (D) Baseline [QGG-QLF] marker vs. response status.
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Fig 5. Baseline and improver gene expression…
Fig 5. Baseline and improver gene expression and pathway enrichment analysis.
(A) 1,062 genes were differentially expressed (p

Fig 6. HRCT trends across improvers and…

Fig 6. HRCT trends across improvers and non-improvers.

(A) QGG trends. (B) QLF trends. (C)…

Fig 6. HRCT trends across improvers and non-improvers.
(A) QGG trends. (B) QLF trends. (C) Baseline [QGG-QLF] marker vs. intrinsic subset. (D) Baseline [QGG-QLF] marker vs. response status.
Fig 6. HRCT trends across improvers and…
Fig 6. HRCT trends across improvers and non-improvers.
(A) QGG trends. (B) QLF trends. (C) Baseline [QGG-QLF] marker vs. intrinsic subset. (D) Baseline [QGG-QLF] marker vs. response status.

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